Results 131 to 140 of about 572,440 (170)
Erratum: Non-local Schrödinger diffusion model reveals mechanisms of critical brain dynamics.
Cell Rep Phys SciDeco G, Perl YS, Kringelbach ML.
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Model for Heterogeneous Diffusion
SIAM Journal on Applied Mathematics, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yong-Jung Kim, Hyowon Seo
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Rouse model in crowded environment modeled by “diffusing diffusivity”
Physica A: Statistical Mechanics and its Applications, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ahamad, Nabi, Debnath, Pallavi
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Science of The Total Environment, 1985
A mathematical model has been developed that examines the ingress of radon into houses, through a vertical crack in an otherwise impervious concrete floor. Initially, the model considered the diffusive flow of radon from its soil source and this simulation has highlighted the dependency of the flux of radon into the house on the magnitude of various ...
P, Wilkinson, P J, Dimbylow
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A mathematical model has been developed that examines the ingress of radon into houses, through a vertical crack in an otherwise impervious concrete floor. Initially, the model considered the diffusive flow of radon from its soil source and this simulation has highlighted the dependency of the flux of radon into the house on the magnitude of various ...
P, Wilkinson, P J, Dimbylow
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2017
In 1950, van Roosbroeck introduced the fundamental semiconductor device equations as a system of three nonlinearly coupled partial differential equations (PDEs). They describe the semiclassical transport of free electrons and holes in a self-consistent electric field using a drift-diffusion approximation.
Farrell P. +5 more
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In 1950, van Roosbroeck introduced the fundamental semiconductor device equations as a system of three nonlinearly coupled partial differential equations (PDEs). They describe the semiclassical transport of free electrons and holes in a self-consistent electric field using a drift-diffusion approximation.
Farrell P. +5 more
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Proceedings of the AAAI Conference on Artificial Intelligence
Diffusion Models (DMs) offer robust tools for addressing uncertainty and enhancing adaptability in robotics. This work explores their application to trajectory generation, 3D image synthesis, and interpretable scene understanding. For trajectory planning, we propose using colored Gaussian noise to improve robustness and temporal coherence.
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Diffusion Models (DMs) offer robust tools for addressing uncertainty and enhancing adaptability in robotics. This work explores their application to trajectory generation, 3D image synthesis, and interpretable scene understanding. For trajectory planning, we propose using colored Gaussian noise to improve robustness and temporal coherence.
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Modelling diffusion and replacement
European Journal of Operational Research, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Towhidul Islam, Nigel Meade
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